Deep Complex-valued Convolutional Learning for Waveform OFDM Receiver Design

Orthogonal frequency division multiplexing (OFD-M) has been widely used in modern communication networks. Notice that OFDM typically relies on (inverse) Discrete Fourier Transform (DFT/IDFT) for processing its waveforms. In this context, we propose a deep learning-based OFDM receiver that uses a deep complex-valued convolutional neural network (DC-CNN) to recover the information bit stream from synchronized time-domain signals without relying on DFT/IDFT. Specifically, a learned linear transform is designed to utilize the cyclic prefix (CP) of OFDM waveforms instead of DFT/IDFT, which presents the ability of DCCNN for complex communication waveforms. To improve the convergence of the training model for the DCCNN-based receiver, a novel transfer learning scheme is developed to train channel equalization and demodulation in two phases. In addition, both the DCCNN equalizer and DCCNN demodulator are trained and tested at different SNRs for Rayleigh fading and noise, and a mixed multiple fading channel model with various delay spreads is utilized to smooth the training loss. Simulation results suggest that our developed DCCNN channel estimator outperforms conventional estimators such as least square (LS), linear minimum mean square error (LMMSE) and low-rank approximation of LMMSE (ALMMSE) in multipath Rayleigh fading models with varying Doppler spreads and delay spreads.